A test of the predator satiation hypothesis, acorn predator size, and acorn preference
Bibliographic record
Abstract
Mast seeding is hypothesized to satiate seed predators with heavy production and reduce populations with crop failure, thereby increasing seed survival. Preference for red or white oak acorns could influence recruitment among oak species. We tested the predator satiation hypothesis, acorn preference, and predator size by concurrently measuring acorn production, mouse abundance, and white versus red oak acorn removal rates in exclosures allowing access by mice (HW), squirrels and smaller-sized vertebrates (WW), or all-sized vertebrates (C) for 12 years. Annual removal rate varied, but virtually all acorns were eventually removed from all exclosure types all years except one. Acorns were removed more slowly from HW than from WW or C exclosures, indicating that large vertebrates were not major acorn consumers, locally. Red and white oak acorn removal rates were similar except in two years, when red oak acorns were removed more rapidly. Removal slowed with increasing acorn crops, suggesting that heavy crops can “swamp” predators. Removal rate was negatively correlated with crop size the previous fall. A positive trend between mouse abundance and crop size the previous fall was evident; abundance decreased sharply the year following crop failures but not after moderate or heavy crops, suggesting that poor crops can dampen acorn predation the following year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".